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RLoptimal: Optimal Adaptive Allocation Using Deep Reinforcement Learning

An implementation to compute an optimal adaptive allocation rule using deep reinforcement learning in a dose-response study (Matsuura et al. (2022) <doi:10.1002/sim.9247>). The adaptive allocation rule can directly optimize a performance metric, such as power, accuracy of the estimated target dose, or mean absolute error over the estimated dose-response curve.

Version: 1.0.1
Imports: DoseFinding, glue, R6, reticulate, stats, utils
Suggests: knitr, rmarkdown
Published: 2024-10-03
DOI: 10.32614/CRAN.package.RLoptimal
Author: Kentaro Matsuura ORCID iD [aut, cre, cph], Koji Makiyama [aut, ctb]
Maintainer: Kentaro Matsuura <matsuurakentaro55 at gmail.com>
BugReports: https://github.com/MatsuuraKentaro/RLoptimal/issues
License: MIT + file LICENSE
URL: https://github.com/MatsuuraKentaro/RLoptimal
NeedsCompilation: no
Language: en_US
Materials: README NEWS
CRAN checks: RLoptimal results

Documentation:

Reference manual: RLoptimal.pdf
Vignettes: Optimal Adaptive Allocation Using Deep Reinforcement Learning (source, R code)

Downloads:

Package source: RLoptimal_1.0.1.tar.gz
Windows binaries: r-devel: RLoptimal_1.0.1.zip, r-release: RLoptimal_1.0.1.zip, r-oldrel: RLoptimal_1.0.1.zip
macOS binaries: r-release (arm64): RLoptimal_1.0.1.tgz, r-oldrel (arm64): RLoptimal_1.0.1.tgz, r-release (x86_64): RLoptimal_1.0.1.tgz, r-oldrel (x86_64): RLoptimal_1.0.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=RLoptimal to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.